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1. Normalized Hurricane Damage in the United States: 1900–2022

   https://doi.org/10.1175/BAMS-D-23-0280.1  

   Muller, Joanne; Mooney, Kaylee; Bowen, Steven G; Klotzbach, Philip J; Martin, Tynisha; Philp, Tom J; Dhruvkumar, Bhatt; Dixon, Richard S; Girimurugan, Senthil B (January 2025, Bulletin of the American Meteorological Society)

   Landsea, C (Ed.)

   Abstract Since 1900, landfalling hurricanes have been the costliest of all weather-related disasters to afflict the contiguous United States. To provide a present-day (2022) reevaluation of this risk, this study employs an improved normalization approach to better understand potential economic event losses in the context of contemporary societal conditions. The updated methodology identifies impacted coastal counties using the newly available radius of maximum winds at landfall. Hurricane Katrina is the most expensive hurricane since 1900, with a likely 2022 normalized cost of $234 billion. Combined losses from the 50 most expensive hurricane events are ∼ $2.9 trillion in normalized economic losses. The study also explores some “analog storms” where comparisons can be made between two historic storms with similar landfall locations. For example, category 5 Andrew (1992) has lower 2022 normalized losses than category 4 Great Miami (1926), at $125 billion versus $178 billion, most likely due to the significantly different radius of maximum wind size (10 vs 20 n mi; 1 n mi = 1.852 km). As with previous studies, we conclude that increases in inflation, coastal population, regional wealth, and higher replacement costs remain the primary drivers of observed increases in hurricane-related damage. These upsurges are especially impactful for some coastal regions along the U.S. Gulf and Southeast Coasts that have seen exceptionally high rates of population/housing growth in comparison to countrywide growth. Exposure growth trends are likely to continue in the future and, independent of any influence of climate change on tropical cyclone behavior, are expected to result in greater hurricane-related damage costs than have been previously observed. 

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2. Dynamic modeling of public and private decision‐making for hurricane risk management including insurance, acquisition, and mitigation policy

   https://doi.org/10.1111/rmir.12215  

   Guo, Cen; Nozick, Linda; Kruse, Jamie; Millea, Meghan; Davidson, Rachel; Trainor, Joseph (June 2022, Risk Management and Insurance Review)

   Abstract We develop a computational framework for the stochastic and dynamic modeling of regional natural catastrophe losses with an insurance industry to support government decision‐making for hurricane risk management. The analysis captures the temporal changes in the building inventory due to the acquisition (buyouts) of high‐risk properties and the vulnerability of the building stock due to retrofit mitigation decisions. The system is comprised of a set of interacting models to (1) simulate hazard events; (2) estimate regional hurricane‐induced losses from each hazard event based on an evolving building inventory; (3) capture acquisition offer acceptance, retrofit implementation, and insurance purchase behaviors of homeowners; and (4) represent an insurance market sensitive to demand with strategically interrelated primary insurers. This framework is linked to a simulation‐optimization model to optimize decision‐making by a government entity whose objective is to minimize region‐wide hurricane losses. We examine the effect of different policies on homeowner mitigation, insurance take‐up rate, insurer profit, and solvency in a case study using data for eastern North Carolina. Our findings indicate that an approach that coordinates insurance, retrofits, and acquisition of high‐risk properties effectively reduces total (uninsured and insured) losses. 

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3. Hurricane Michael Altered the Structure and Function of Longleaf Pine Woodlands

   https://doi.org/10.1029/2021JG006452  

   Kenney, G.; Staudhammer, C. L.; Wiesner, S.; Brantley, S. T.; Bigelow, S. W.; Starr, G. (December 2021, Journal of Geophysical Research: Biogeosciences)

   Abstract Tropical cyclones can physically alter ecosystems, causing immediate and potentially long‐lasting effects on carbon dynamics. In 2018, Hurricane Michael hit the southeastern United States with category 5 winds at landfall and category 2 winds reaching over 100 miles inland, resulting in extensive damage. Longleaf pine woodlands in the path of the hurricane were damaged, but severity varied based on the storm track. We used a combination of eddy covariance measurements, airborne LiDAR, and forest inventory data to determine whether hurricane affects structure, function, and recovery of two longleaf pine woodlands at the ends of an edaphic gradient. We found that the carbon sink potentials in both sites were diminished following the storm, with reductions in net ecosystem exchange (NEE) primarily due to lower rates of photosynthesis, as respiration only increased marginally. The xeric site carbon losses and physiological reductions were smaller following the disturbance, which led to the recovery of ecosystem physiological activity to prestorm rates before that of the mesic site, as indicated by maximum ecosystem CO₂uptake rates. Two years following the hurricane both stands continued to have reduced NEE, which signaled altered function. We expect both locations to recover their lost carbon stocks in ∼10–35 years; however, long‐term studies are needed to examine how longleaf woodlands respond to compounding disturbances, such as drought, fire, or other wind storms, which vary significantly across the ecosystem's range. Additionally, hurricanes are intensifying due to climate change, potentially amplifying the degree to which they will alter this ecosystem in the future. 

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4. Rent affordability after hurricanes: Longitudinal evidence from US coastal states

   https://doi.org/10.1111/risa.14224  

   Best, Kelsea B.; He, Qian; Reilly, Allison; Tran, Nhi; Niemeier, Deb (October 2023, Risk Analysis)

   Abstract Climate change is expected to increase the frequency and intensity of natural hazards such as hurricanes. With a severe shortage of affordable housing in the United States, renters may be uniquely vulnerable to disaster‐related housing disruptions due to increased hazard exposure, physical vulnerability of structures, and socioeconomic disadvantage. In this work, we construct a panel dataset consisting of housing, socioeconomic, and hurricane disaster data from counties in 19 states across the East and Gulf Coasts of the United States from 2009 to 2018 to investigate how the frequency and intensity of a hurricane correspond to changes in median rent and housing affordability (the interaction between rent prices and income) over time. Using a two‐stage least square random‐effects regression model, we find that more intense prior‐year hurricanes correspond to increases in median rents via declines in housing availability. The relationship between hurricanes and rent affordability is more complex, though the occurrence of a hurricane in a given year or the previous year reduces affordable rental housing, especially for counties with higher percentages of renters and people of color. Our results highlight the multiple challenges that renters are likely to face following a hurricane, and we emphasize that disaster recovery in short‐ and medium‐term should focus on providing safe, stable, and affordable rental housing assistance. 

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5. Modeling post-disaster recovery: Accounting for rental and multi-family housing

   https://doi.org/10.1177/87552930231222769  

   Mongold, Emily; Costa, Rodrigo; Zsarnóczay, Ádám; Baker, Jack_W (January 2024, Earthquake Spectra)

   Post-disaster housing recovery models increase our understanding of recovery dynamics, vulnerable populations, and how people are affected by the direct losses that disasters create. Past recovery models have focused on single-family owner-occupied housing, while empirical evidence shows that rental units and multi-family housing are disadvantaged in post-disaster recovery. To fill this gap, this article presents an agent-based housing recovery model that includes the four common type–tenure combinations of single- and multi-family owner- and renter-occupied housing. The proposed model accounts for the different recovery processes, emphasizing funding sources available to each type–tenure. The outputs of our model include the timing of financing and recovery at building resolution across a community. We demonstrate the model with a case study of Alameda, California, recovering from a simulated M7.0 earthquake on the Hayward fault. The processes in the model replicate higher non-recovery of multi-family housing than single-family housing, as observed in past disasters, and a heavy reliance of single-family renter-occupied units on Small Business Administration funding, which is expected due to low earthquake insurance penetration. The simulation results indicate that multi-family housing would have the highest portion of unmet need remaining; however, some buildings with unmet needs are anticipated to be able to obtain a large portion of their funding. The remaining portion may be filled using personal financing or may be overcome with downsizing or downgrades. Multi-family housing would also benefit the most from Community Development Block Grants for Disaster Recovery (CDBG-DR). This benefit is a result of modeling the financing sources, that CDBG-DR is available, and that many multi-family buildings do not qualify for other sources. Communities’ allocation of public funding is important for housing recovery. Our model can help inform and compare potential financing policies to allocate public funds. 

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